Exposure to agrochemicals in the aquatic environment often occurs as time-varying or repeated pulses. Time-varying exposures may occur due to runoff events and spray drift associated with precipitation and application events. Hydrologic dilution, dispersion, and degradation also produce pulsed exposures. Standard laboratory toxicity tests using constant exposure concentrations typically do not investigate the toxicity of time-varying or repeated exposures. Detoxification, elimination, and recovery may occur within organisms or populations during the periods between exposures. The difficulty of estimating effects of realistic time-varying exposures from measurements made under constant exposure conditions is often an important source of uncertainty in ecological risk assessment of pesticides. This article discusses the criteria and tools for deciding whether time-varying exposures are relevant in a particular risk assessment, approaches for laboratory toxicity testing with time-varying exposure, modeling approaches for addressing effects oftime-varying exposure, deterministic and probabilistic ecological risk characterization of time-varyingexposures and toxicity, and uncertainty analysis.

Abstract. The Geostationary Trace gas and Aerosol Sensor Optimization (GeoTASO) airborne instrument is a test bed for upcoming air quality satellite instruments that will measure backscattered ultraviolet, visible and near-infrared light from geostationary orbit. GeoTASO flew on the NASA Falcon aircraft in its first intensive field measurement campaign during the Deriving Information on Surface Conditions from Column and Vertically Resolved Observations Relevant to Air Quality (DISCOVER-AQ) Earth Venture Mission over Houston, Texas, in September 2013. Measurements of backscattered solar radiation between 420 and 465 nm collected on 4 days during the campaign are used to determine slant column amounts of NO 2 at 250 m × 250 m spatial resolution with a fitting precision of 2.2 × 10 15 molecules cm −2 . These slant columns are converted to tropospheric NO 2 vertical columns using a radiative transfer model and trace gas profiles from the Community Multiscale Air Quality (CMAQ) model. Total column NO 2 from GeoTASO is well correlated with ground-based Pandora observations (r = 0.90 on the most polluted and cloud-free day of measurements and r = 0.74 overall), with GeoTASO NO 2 slightly higher for the most polluted observations. Surface NO 2 mixing ratios inferred from GeoTASO using the CMAQ model show good correlation with NO 2 measured in situ at the surface during the campaign (r = 0.85). NO 2 slant columns from GeoTASO also agree well with preliminary retrievals from the GEO-CAPE Airborne Simulator (GCAS) which flew on the NASA King Air B200 (r = 0.81, slope = 0.91). Enhanced NO 2 is resolvable over areas of traffic NO x emissions and near individual petrochemical facilities.

Houston, Texas is a major U.S. urban and industrial area where poor air quality is unevenly distributed and a disproportionate share is located in low-income, non-white, and Hispanic neighborhoods. We have traditionally lacked city-wide observations to fully describe these spatial heterogeneities in Houston and in cities globally, especially for reactive gases like nitrogen dioxide (NO 2 ). Here, we analyze novel high-spatial-resolution (250 m × 500 m) NO 2 vertical columns measured by the NASA GCAS airborne spectrometer as part of the September-2013 NASA DISCOVER-AQ mission and discuss differences in population-weighted NO 2 at the census-tract level. Based on the average of 35 repeated flight circuits, we find 37 ± 6% higher NO 2 for non-whites and Hispanics living in low-income tracts (LIN) compared to whites living in highincome tracts (HIW) and report NO 2 disparities separately by race ethnicity (11−32%) and poverty status (15−28%). We observe substantial time-of-day and day-to-day variability in LIN-HIW NO 2 differences (and in other metrics) driven by the greater prevalence of NO x (NO + NO 2 ) emission sources in low-income, non-white, and Hispanic neighborhoods. We evaluate measurements from the recently launched satellite sensor TROPOMI (3.5 km × 7 km at nadir), averaged to 0.01°× 0.01°using physics-based oversampling, and demonstrate that TROPOMI resolves similar relative, but not absolute, tract-level differences compared to GCAS. We utilize the high-resolution FIVE and NEI NO x inventories, plus one year of TROPOMI weekday−weekend variability, to attribute tract-level NO 2 disparities to industrial sources and heavy-duty diesel trucking. We show that GCAS and TROPOMI spatial patterns correspond to the surface patterns measured using aircraft profiling and surface monitors. We discuss opportunities for satellite remote sensing to inform decision making in cities generally.

NASA deployed the GeoTASO airborne UVvisible spectrometer in May-June 2017 to produce highresolution (approximately 250 m × 250 m) gapless NO 2 datasets over the western shore of Lake Michigan and over the Los Angeles Basin. The results collected show that the airborne tropospheric vertical column retrievals compare well with ground-based Pandora spectrometer column NO 2 observations (r 2 = 0.91 and slope of 1.03). Apparent disagreements between the two measurements can be sensitive to the coincidence criteria and are often associated with large local variability, including rapid temporal changes and spatial heterogeneity that may be observed differently by the sunward-viewing Pandora observations. The gapless mapping strategy executed during the 2017 GeoTASO flights provides data suitable for averaging to coarser areal resolutions to simulate satellite retrievals. As simulated satellite pixel area increases to values typical of TEMPO (Tropospheric Emissions: Monitoring Pollution), TROPOMI (TRO-POspheric Monitoring Instrument), and OMI (Ozone Monitoring Instrument), the agreement with Pandora measurements degraded, particularly for the most polluted columns as localized large pollution enhancements observed by Pandora and GeoTASO are spatially averaged with nearby less-polluted locations within the larger area representative of the satellite spatial resolutions (aircraft-to-Pandora slope: TEMPO scale = 0.88; TROPOMI scale = 0.77; OMI scale = 0.57). In these two regions, Pandora and TEMPO or TROPOMI have the potential to compare well at least up to pollution scales of 30 × 10 15 molecules cm −2 . Two publicly available OMI tropospheric NO 2 retrievals are found to be biased low with respect to these Pandora observations. However, the agreement improves when higher-resolution a priori inputs are used for the tropospheric air mass factor calculation (NASA V3 standard product slope = 0.18 and Berkeley High Resolution product slope = 0.30). Overall, this work explores best practices for satellite validation strategies with Pandora direct-sun observations by showing the sensitivity to product spatial resolution and demonstrating how the high-spatial-resolution NO 2 data retrieved from airborne spectrometers, such as GeoTASO, can be used with high-temporal-resolution ground-based column observationsPublished by Copernicus Publications on behalf of the European Geosciences Union. 6092 L. M. Judd et al.: Impact of spatial resolution on tropospheric NO 2 column comparisons to evaluate the influence of spatial heterogeneity on validation results.

Abstract. Airborne and ground-based Pandora spectrometer NO2 column measurements were collected during the 2018 Long Island Sound Tropospheric Ozone Study (LISTOS) in the New York City/Long Island Sound region, which coincided with early observations from the Sentinel-5P TROPOspheric Monitoring Instrument (TROPOMI) instrument. Both airborne- and ground-based measurements are used to evaluate the TROPOMI NO2 Tropospheric Vertical Column (TrVC) product v1.2 in this region, which has high spatial and temporal heterogeneity in NO2. First, airborne and Pandora TrVCs are compared to evaluate the uncertainty of the airborne TrVC and establish the spatial representativeness of the Pandora observations. The 171 coincidences between Pandora and airborne TrVCs are found to be highly correlated (r2= 0.92 and slope of 1.03), with the largest individual differences being associated with high temporal and/or spatial variability. These reference measurements (Pandora and airborne) are complementary with respect to temporal coverage and spatial representativity. Pandora spectrometers can provide continuous long-term measurements but may lack areal representativity when operated in direct-sun mode. Airborne spectrometers are typically only deployed for short periods of time, but their observations are more spatially representative of the satellite measurements with the added capability of retrieving at subpixel resolutions of 250 m × 250 m over the entire TROPOMI pixels they overfly. Thus, airborne data are more correlated with TROPOMI measurements (r2=0.96) than Pandora measurements are with TROPOMI (r2=0.84). The largest outliers between TROPOMI and the reference measurements appear to stem from too spatially coarse a priori surface reflectivity (0.5∘) over bright urban scenes. In this work, this results during cloud-free scenes that, at times, are affected by errors in the TROPOMI cloud pressure retrieval impacting the calculation of tropospheric air mass factors. This factor causes a high bias in TROPOMI TrVCs of 4 %–11 %. Excluding these cloud-impacted points, TROPOMI has an overall low bias of 19 %–33 % during the LISTOS timeframe of June–September 2018. Part of this low bias is caused by coarse a priori profile input from the TM5-MP model; replacing these profiles with those from a 12 km North American Model–Community Multiscale Air Quality (NAMCMAQ) analysis results in a 12 %–14 % increase in the TrVCs. Even with this improvement, the TROPOMI-NAMCMAQ TrVCs have a 7 %–19 % low bias, indicating needed improvement in a priori assumptions in the air mass factor calculation. Future work should explore additional impacts of a priori inputs to further assess the remaining low biases in TROPOMI using these datasets.

The GEOstationary Coastal and Air Pollution Events (GEO-CAPE) Airborne Simulator (GCAS) was developed in support of NASA's decadal survey GEO-CAPE geostationary satellite mission. GCAS is an airborne pushbroom remote-sensing instrument, consisting of two channels which make hyperspectral measurements in the ultraviolet/visible (optimized for air quality observations) and the visible-near infrared (optimized for ocean color observations). The GCAS instrument participated in its first intensive field campaign during the Deriving Information on Surface Conditions from Column and Vertically Resolved Observations Relevant to Air Quality (DISCOVER-AQ) campaign in Texas in September 2013. During this campaign, the instrument flew on a King Air B-200 aircraft during 21 flights on 11 days to make air quality observations over Houston, Texas. We present GCAS trace gas retrievals of nitrogen dioxide (NO 2 ) and formaldehyde (CH 2 O), and compare these results with trace gas columns derived from coincident in situ profile measurements of NO 2 and CH 2 O made by instruments on a P-3B aircraft, and with NO 2 observations from ground-based Pandora spectrometers operating in direct-sun and scattered light modes. GCAS tropospheric column measurements correlate well spatially and temporally with columns estimated from the P-3B measurements for both NO 2 (r 2 = 0.89) and CH 2 O (r 2 = 0.54) and with Pandora direct-sun (r 2 = 0.85) and scattered light (r 2 = 0.94) observed NO 2 columns. Coincident GCAS columns agree in magnitude with NO 2 and CH 2 O P-3B-observed columns to within 10 % but are larger than scattered lightPublished by Copernicus Publications on behalf of the European Geosciences Union. 5942 C. R. Nowlan et al.: GCAS measurements of NO 2 and CH 2 O Pandora tropospheric NO 2 columns by 33 % and direct-sun Pandora NO 2 columns by 50 %.

With the near-future launch of geostationary Earth orbit (GEO) pollution monitoring satellite instruments over North America, East Asia, and Europe, the air quality community is preparing for an integrated global atmospheric composition observing system at unprecedented spatial and temporal resolutions. One of the ways that NASA has supported this community preparation is through demonstration of future space-borne capabilities using the Geostationary Trace gas and Aerosol Sensor Optimization (GeoTASO) airborne instrument. This paper integrates repeated high-resolution NO 2 maps from GeoTASO, ground-based Pandora spectrometers data, and low Earth orbit (LEO) measurements from the Ozone Mapping and Profiler Suite, for case studies over two regions: the Seoul Metropolitan Area, South Korea on June 9th, 2016 and Los Angeles Basin, California on June 27th, 2017. This dataset provides a unique opportunity to illustrate how GEO air quality monitoring platforms and ground-based remote sensing networks will close the current spatiotemporal observation gap. In both areas, the earliest morning maps exhibit spatial patterns similar to emission source areas (e.g., urbanized valleys, roadways, major airports) and change later in the day due to boundary layer dynamics, transport, and/or chemistry. On June 9th, 2016, GeoTASO observes NO 2 accumulating within the Seoul Metropolitan Area, while NO 2 peaks in the morning and decreases throughout the afternoon in the Los Angeles Basin on June 27th, 2017. The nominal resolution of GeoTASO is finer than will be obtained from GEO platforms, but when NO 2 data over Los Angeles are up-scaled to the expected resolution of TEMPO, spatial features discussed are preserved. Pandora instruments installed in both metropolitan areas capture the diurnal patterns observed by GeoTASO, continuously and over longer time periods and will play a critical role in validation of the next generation of satellite measurements. These case studies demonstrate the diversity of diurnal patterns in two urbanized regions and associates them with meteorology Judd et al.Dawn of Geostationary Air Quality Monitoring or anthropogenic patterns, hinting at the spatial and temporal richness of the upcoming GEO observations. LEO measurements, despite their inability to capture the diurnal patterns at fine spatial resolution, will be essential for intercalibrating the GEO radiances and cross-validating the GEO retrievals in an integrated global observing system.

Abstract. Nitrogen dioxide vertical column density (NO2 VCD) measurements via satellite are compared with a fine-scale regional chemistry transport model, using a new approach that considers varying satellite footprint sizes. Space-borne NO2 VCD measurement has been used as a proxy for surface nitrogen oxide (NOx) emission, especially for anthropogenic urban emission, so accurate comparison of satellite and modeled NO2 VCD is important in determining the future direction of NOx emission policy. The NASA Ozone Monitoring Instrument (OMI) NO2 VCD measurements, retrieved by the Royal Netherlands Meteorological Institute (KNMI), are compared with a 12 km Community Multi-scale Air Quality (CMAQ) simulation from the National Oceanic and Atmospheric Administration. We found that the OMI footprint-pixel sizes are too coarse to resolve urban NO2 plumes, resulting in a possible underestimation in the urban core and overestimation outside. In order to quantify this effect of resolution geometry, we have made two estimates. First, we constructed pseudo-OMI data using fine-scale outputs of the model simulation. Assuming the fine-scale model output is a true measurement, we then collected real OMI footprint coverages and performed conservative spatial regridding to generate a set of fake OMI pixels out of fine-scale model outputs. When compared to the original data, the pseudo-OMI data clearly showed smoothed signals over urban locations, resulting in roughly 20–30 % underestimation over major cities. Second, we further conducted conservative downscaling of OMI NO2 VCDs using spatial information from the fine-scale model to adjust the spatial distribution, and also applied averaging kernel (AK) information to adjust the vertical structure. Four-way comparisons were conducted between OMI with and without downscaling and CMAQ with and without AK information. Results show that OMI and CMAQ NO2 VCDs show the best agreement when both downscaling and AK methods are applied, with the correlation coefficient R = 0.89. This study suggests that satellite footprint sizes might have a considerable effect on the measurement of fine-scale urban NO2 plumes. The impact of satellite footprint resolution should be considered when using satellite observations in emission policy making, and the new downscaling approach can provide a reference uncertainty for the use of satellite NO2 measurements over most cities.